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Fourier EGARCH: Modelagem de Volatilidade com Quebras Estruturais Suaves×GJR-GARCH (GARCH Assimétrico)×
ÁreaEconometriaEconometria
FamíliaRegression modelRegression model
Ano de origem2010s1993
Autor originalExtension of Nelson (1991) EGARCH using Fourier approximation frameworksGlosten, Jagannathan & Runkle (1993); Zakoian (1994)
TipoVolatility model with smooth structural breaksAsymmetric conditional volatility model
Fonte seminalEnders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574-599. DOI ↗Glosten, L. R., Jagannathan, R. & Runkle, D. E. (1993). On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5), 1779-1801. DOI ↗
Outros nomesFourier-EGARCH, F-EGARCH, Fourier exponential GARCH, smooth structural break EGARCHasymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle)
Relacionados35
ResumoFourier EGARCH extends Nelson's (1991) Exponential GARCH model by embedding Fourier trigonometric terms in the conditional variance equation to capture smooth, gradual shifts in the unconditional variance level over time. This allows the model to handle structural breaks in volatility without requiring prior knowledge of their timing or number.GJR-GARCH is a variant of the GARCH conditional-volatility model that captures the asymmetric effect of negative shocks on volatility using an indicator variable. It was introduced by Glosten, Jagannathan and Runkle (1993), with a closely related threshold formulation by Zakoian (1994).
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ScholarGateComparar métodos: Fourier EGARCH · GJR-GARCH. Recuperado em 2026-06-19 de https://scholargate.app/pt/compare